84results about How to "Reduce subjectivity" patented technology

The invention belongs to the technical field of knowledge graph construction, and particularly relates to a multi-source heterogeneous data fusion and knowledge graph automatic construction system, which comprises a semantic extraction module for extracting minimum semantic fragments from multi-source data and performing cross-source alignment to generate a symbolized semantic framework; the data purification module converts multi-source data into symbolic predicates according to the framework, filters low-evidence data and outputs a purification set; the ternary generation module inputs the initial triad candidate set into a minimum rule grammar, and extracts entities, relationships and attributes to generate an initial triad candidate set; the disambiguation calibration module generates entity two-hop topological fingerprints based on the candidate set, completes disambiguation alignment and corrects conflicts, and outputs unambiguous structured knowledge; the mapping fusion module fuses the mapping information with the minimum connected ontology, and constructs and verifies an initial mapping knowledge domain; and the incremental updating module processes newly added data, performs incremental merging and maintains consistency, and forms a complete knowledge graph. According to the method, through full-link automatic construction, efficient fusion and high-quality atlas are realized.

The invention discloses a design manuscript code generation and visual error iterative correction method and system based on multi-agent cooperation, and aims to solve the problems of low front-end code generation efficiency, poor visual restoration degree and lack of an automatic correction mechanism in the prior art. The system constructs a four-node state diagram based on LangGraph, automatically initializes a series engineering scaffold, obtains design data and generates a structured protocol, and generates a component code based on the protocol and performs visual verification and iterative correction on four processing nodes. The method comprises the following steps: dynamically generating an engineering scaffold through a large language model agent, converting Figma design data into a structured protocol, and carrying out iterative optimization by utilizing a judger-repairer mode until an error is lower than a threshold value; according to the method, high-fidelity automatic delivery from the design manuscript to the code is realized, and the executable front-end engineering code and the visual reduction degree analysis report are output.

The invention discloses a feature analysis and prediction method for drawing a depression patient based on a convolutional neural network, and belongs to the field of artificial intelligence and mental health. The method comprises the following steps: acquiring drawing image data of a depression patient and a healthy control group, and constructing a standardized data set; preprocessing such as denoising, size normalization and edge enhancement is carried out on the image; constructing a convolutional neural network model comprising four convolutional layers, two pooling layers and a feature fusion layer, and automatically extracting key features such as line definition, spatial layout and detail richness by adopting a variable receptive field mechanism and an attention module; correlation analysis is carried out in combination with scores of a clinical evaluation scale, and the model is optimized through transfer learning and a focus loss function; and generating a risk assessment report. According to the method, objectivity and accuracy of depression screening are remarkably improved, subjective evaluation deviation is reduced, visual decision support is provided for clinicians, and early recognition and intervention of depression are achieved.

The present application belongs to the technical field of material corrosion data processing and industrial equipment state monitoring, and provides a kind of multi-source feature extraction and structured expression method of air conditioner heat exchanger corrosion data, specifically comprising the following steps: obtaining multi-source corrosion original data;Data preprocessing;Corrosion morphology feature extraction;Electrochemical feature extraction;Environmental influence feature extraction;Feature normalization processing;Structured feature vector construction;Form a structured feature vector with fixed dimension, unified expression. Each component in the feature vector corresponds to a corrosion-related parameter with clear physical meaning, completely describing the corrosion state, corrosion kinetics characteristics and service environment conditions of the heat exchanger under specific space-time conditions. Advantage: significantly improve the usability of data, provide an ideal data interface for multi-modal data fusion analysis and large model technology application, significantly improve the objectivity and reliability of the analysis results, have strong engineering practical value.

The invention relates to the technical field of straightening equipment, and discloses an automatic matching straightening machine for shaft workpieces, which comprises a machine body, a plurality of linear sliding rails for a plurality of mechanisms to linearly move are arranged on the machine body, and strip-shaped tooth grooves matched with moving gears are formed in the two sides of the interior of the machine body; one side of the machine body is provided with a PLC (Programmable Logic Controller) control cabinet for calculating input shaft workpiece data information and generating a control signal, and the PLC control cabinet is used for controlling each mechanism on the machine body in an operation process. By adding and arranging the movable detection mechanism, when the shaft workpiece is straightened, the movable detection mechanism adopts a lever type detection structure, can be tightly attached to the surface of the shaft workpiece, sensitively captures bending deformation information of the workpiece, realizes accurate acquisition of deformation, avoids subjectivity and errors of manual detection, and improves the straightening accuracy of the shaft workpiece. And full-length movement detection can be realized along a linear sliding rail, all parts of the shaft workpiece are covered, and the condition of missing detection of a deformation position is avoided.

This invention discloses a method and system for labeling prompt words in a large-scale communication domain model based on knowledge graphs. Belonging to the fields of communication technology and artificial intelligence, it addresses the following technical problems: low efficiency in labeling specialized terms in communication domain prompt word annotation, low accuracy and efficiency of automatic recognition, limited prompt word dimensions, and lagging domain knowledge. The method includes: sequence labeling and entity extraction using a trained combined model to output entities; generating relation triples as candidate relations using a trained generator, and scoring and ranking the selected relation triples using a trained discriminator; semantic parsing of user input to extract key entities and intents; multi-hop reasoning from a dynamic knowledge graph to retrieve relevant entities and relations; eliminating illusory content using a trained GAN network; generating entity prompt words from three levels: basic, extended, and application dimensions; and introducing a triple quality verification mechanism.

The invention belongs to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel roof disaster real-time early warning system and method. Comprising the following steps: performing time sequence reasoning on monitoring data acquired in real time, respectively judging whether the key physical quantity of the roof triggers primary early warning or secondary early warning by acquiring an expected value prediction curve and an uncertainty boundary curve of the key physical quantity of the roof in a future time step, and acquiring corresponding early warning information when the early warning is triggered; constructing a dynamic causal graph, inputting the early warning information and the currently acquired key physical quantity into the dynamic causal graph for reasoning, calling candidate intervention measures based on the acquired current state node to perform anti-fact reasoning in the dynamic causal graph, and acquiring a risk evolution trajectory and a risk suppression efficiency index of the candidate intervention measures; and the risk suppression efficiency indexes are sorted and preferentially selected, and a causal decision early warning report containing the optimal intervention scheme is output, so that the scientificity and effectiveness of risk suppression are improved.

The invention provides a three-dimensional organ reconstruction registration fusion method based on a multi-modal medical image, and belongs to the technical field of medical images. Comprising the following steps: S1, collecting a multi-modal medical image of an ith organ of a target patient in real time, and constructing a preoperative data set; s2, after the first data set is constructed, continuously constructing an intra-operative data set through the intra-operative ultrasonic image; s3, constructing an AI model by using a convolutional neural network; s4, based on the preoperative data set and the intraoperative data set, constructing a stability coefficient and a crowding degree coefficient of the ith organ; and S5, associating the stability coefficient of the ith organ with the crowding degree coefficient of the ith organ, and constructing a comprehensive dynamic risk coefficient. According to the method, CT, MRI and intraoperative ultrasonic images are collected at the same time, the preoperative data set is constructed, the organ volume change rate and the body position adjustment displacement are updated in real time in the operation, the optimal operation path can be automatically predicted, and the subjectivity of depending on doctor experience in traditional operation path planning is reduced.

This invention relates to the technical field of industrial automated mechanical assembly, and more particularly to an auxiliary device for assembling a smart alarm clock. The device includes a frame with a worktable having an alarm clock placement slot for positioning the smart alarm clock housing; a translation component mounted on the frame for driving a locking unit to switch between a locking position and a pick-and-place position; a lifting component mounted on the translation component for driving the locking unit to approach or move away from the alarm clock housing; and a rotation drive component mounted on the lifting component for outputting rotational power. This invention uses a stepped arc-shaped adjustment plate to progressively change the spring preload, adjusting the locking force in stages to achieve multi-level adjustable torque. Operators can manually rotate the adjustment ring to quickly adapt to the locking requirements of different alarm clock models, solving the problem of inflexible adjustment of existing single-level fixed torque and significantly improving the versatility and adaptability of the equipment.

The application is suitable for the technical field of network drama auditing, and provides an artificial intelligence full-process auditing method and system for network drama, which comprises the following steps: analyzing a script based on a natural language model, and constructing a script knowledge graph comprising a macro-story layer, a meso-plot layer and a micro-element layer; the macro-story layer is used for representing the core theme and value orientation of the whole drama; the meso-plot layer automatically divides the script into multiple plot units; the micro-element layer is used for representing the entity elements and element development track within each plot unit; based on the script knowledge graph, dynamic risk identification is performed on the script to determine potential risk plot units; the rough cut sample and the script knowledge graph are aligned, and the risk index of the sample segment corresponding to the potential risk plot unit is calculated. The multi-level script knowledge graph constructed based on the natural language model can comprehensively and systematically understand the script content, and improves the accuracy and consistency of the auditing result.

This invention discloses an industrial material inspection method and system based on feature extraction and contrast enhancement, belonging to the field of image inspection technology. It includes: S1, acquiring the transmitted signal using a digital flat panel detector and generating an original digital image based on the differences in X-ray absorption by different materials or defects; S2, performing uniformity correction and normalization enhancement on the original digital image, and achieving preliminary differentiation of different material regions; S3, dividing the original image data into processing domains based on the preliminary differentiation results, and performing differentiated enhancement processing based on the local feature attributes of each processing domain to generate a binary feature image. By integrating X-ray penetration imaging, multi-dimensional image enhancement, and a deep learning-driven intelligent rating system, this invention fundamentally solves the key technical bottlenecks in traditional industrial material inspection, such as inconsistent image quality, lack of material identification, and strong subjectivity in rating, achieving an automated, intelligent, and objective upgrade in industrial material defect detection.

The application relates to a personalized privacy risk dynamic measurement method based on a deep neural network, and specifically comprises the following steps: S1, collecting personalized privacy demands of a user, and establishing a personalized user privacy demand vector; S2, according to the personalized user privacy demand vector, extracting a feature matrix in a user picture by using a convolutional neural network, calculating a privacy feature vector from the feature matrix by using the convolutional neural network, multiplying the privacy feature vector with the personalized user privacy demand vector, obtaining a personalized privacy feature vector of the user in the picture, and identifying sensitive privacy information in the picture; and S3, predicting a leakage risk of the personalized privacy feature vector. The application is designed based on a deep neural network, solves the problem of privacy risk assessment from the perspective of user shared privacy image historical behavior characteristics, measures the risk size of user privacy information leakage, reduces the user privacy information leakage risk from the source, and enhances the user privacy consciousness.

The application provides a method for determining a cell restraint gap, a cell, a battery pack and a power utilization device, and solves the problem of long cycle and high cost consumption in the prior art by relying on the experience trial and error method to determine the restraint gap. The risk determination method comprises: obtaining a cell thickness of a to-be-restrained cell in a restraint direction that meets a preset requirement; and determining the restraint gap of the to-be-restrained cell according to the cell thickness through a preset nonlinear function relationship. The method establishes a preset nonlinear function relationship between the cell thickness and the restraint gap, so that under the premise of meeting the specific structure condition, the optimal restraint gap suitable for the cell can be quickly and accurately determined without relying on the traditional multi-gradient trial and error verification.

The application relates to the technical field of retrieval enhancement generation evaluation, and provides a retrieval enhancement evaluation method and device based on multi-round dialogue context perception, which comprises the following steps: a first large language model is constructed; the first large language model is used for question rewriting according to a question and context; historical multi-round question and answer data of real users is extracted; a multi-round evaluation set is generated based on the historical multi-round question and answer data, a knowledge base document, a second large language model and a RAG system; the second large language model is used for summarizing a business classification, a question type and an interaction paradigm of each multi-round question and answer; the first large language model is used for converting the multi-round evaluation set into a single-round question set; and the RAG system is evaluated by using evaluation indexes, the multi-round evaluation set and the single-round question set. The application realizes automatic generation of the multi-round evaluation set, reduces the manual labeling cost, reduces the construction difficulty of the multi-round evaluation set, and realizes evaluation of the multi-round dialogue RAG task.

This invention relates to the field of container loading optimization technology, and particularly to a container loading optimization method and system considering palletizing. The method includes setting container information, palletizing constraints, and cargo information; generating available pallet sizes and corresponding stack shapes; eliminating stack shapes with bottom coverage below a threshold; using the selected stack shapes to form transverse pallets that meet preset requirements; establishing a mathematical model and solving it using integer programming to obtain an initial loading scheme; performing mixed pallet loading on the remaining container space and remaining cargo, ensuring that the mixed pallet loading meets the requirements of full pallet coverage and full support; generating the mixed pallet loading result and a three-dimensional visualization model. This invention incorporates the entire palletizing-related calculation process into the optimization algorithm to solve the technical problems of cumbersome, inefficient, inaccurate, poor space utilization, and high labor costs in existing container loading calculation processes.

This invention relates to the field of physical oceanography, specifically to a method for rapidly identifying the upper and lower boundaries of the ocean thermocline. The method utilizes trigonometric functions as basis functions to construct a 10th-order trigonometric polynomial, and obtains the fitting parameters and results using the least squares method. Then, the first derivative of the trigonometric polynomial is calculated. The maximum value and its location, as well as all minimum values ​​and their locations, are determined. From the location of the maximum value, the two locations of the first minimum values ​​less than 1 / 10 of the maximum value are found on both sides. This yields the upper and lower boundaries of the ocean thermocline depth. This invention eliminates the subjectivity of parameter selection by eliminating the need for manually setting thresholds; it also avoids complex transformation and construction processes, resulting in a clear algorithm structure and high computational efficiency. It can operate stably in different sea areas and under various data precision conditions, significantly improving the automation, robustness, and computational efficiency of thermocline identification.

The application discloses a VCSEL cold cavity three-dimensional vector mode solving method based on a transmission matrix-finite element hybrid. The method firstly constructs a complete epitaxial structure transmission matrix model containing an oxidation limiting layer to search for a resonance wavelength, and calculates DBR surface admittance at the resonance wavelength; then only the core active region is subjected to three-dimensional structured grid division and Nédélec edge element vector finite element discretization, and the DBR surface admittance is embedded into the weak form of Maxwell equation as an impedance boundary condition; a generalized non-hermitian eigenvalue problem is solved to obtain a cold cavity mode; finally, LP mode automatic classification is realized through a three-level algorithm of aperture restriction factor screening, azimuth angle Fourier decomposition and radial node counting. The application equivalently processes the DBR by the transmission matrix method, reduces the FEM calculation domain by one to two orders of magnitude, greatly improves the calculation efficiency while maintaining the three-dimensional vector precision, and correctly describes the influence of the longitudinal position of the oxide on the resonance condition.

The invention relates to the technical field of artificial intelligence in assisted reproduction, in particular to an embryo optimization model construction method, device and equipment and a storage medium. Comprising the following steps: acquiring video data of embryonic development from a plurality of fertilized eggs to a blastocyst stage based on a delayed photography embryo culture technology; constructing time sequence image data according to the video data; the time sequence image data comprises multiple frames of continuous images; performing feature extraction on each frame of image of the time sequence image to obtain a high-dimensional feature corresponding to each frame of image; determining a comparison difference feature with the corresponding reference sample feature, performing fusion, and constructing a fusion feature corresponding to each frame of image; constructing a feature sequence of the target embryo development according to the fusion features; and acquiring a trained embryo quality grading model according to a time dependency relationship of the feature sequence for capturing embryo development. The model provided by the invention can optimize the utilization rate of embryos, reduce unnecessary repeated ovulation promoting cycles, and promote the development of reproductive medicine towards the precision direction.

The application provides a kind of three-dimensional induced polarization inversion and target identification method based on deep learning, comprising: preprocessing three-dimensional induced polarization observation data collected by multiple sources to obtain preprocessed induced polarization data;Based on non-structural multi-scale grid, a three-dimensional induced polarization forward and inversion model is constructed, the preprocessed induced polarization data is input into the three-dimensional induced polarization forward and inversion model, and iterative solution is carried out combined with grid self-adaptive encryption and joint inversion objective function to generate three-dimensional electrical property characteristic data;Apply mixed regularization constraint to three-dimensional electrical property characteristic data to generate high-resolution three-dimensional electrical property data volume;The mixed regularization constraint contains smoothing constraint and total variation constraint, and the weight proportion of the smoothing constraint and the total variation constraint is dynamically adjusted according to the detection depth;Input the high-resolution three-dimensional electrical property data volume into the deep learning identification model to output the target identification result.The application effectively eliminates the false anomaly of complex fault structure, greatly improves the accuracy and efficiency of deep mineral exploration, and is suitable for fine exploration scene of deep deposit.

This application relates to the field of medical data processing technology and discloses a method and system for screening infertile patients with polycystic ovary syndrome (PCOS). The method includes: acquiring data on the number of antral follicles, anti-Müllerian hormone (AMH) concentration, and ovarian volume from multiple patient monitoring cycles; performing feature extraction and normalization to obtain a set of normalized feature vectors; determining the synchronous relationship between the number of antral follicles and ovarian volume based on the vector set, and identifying signs of asynchronous changes; combining hormone concentrations for joint grouping to obtain the distribution of abnormal indicator clusters and identifying ovarian function stage markers; performing sequence matching based on the stage markers of continuous cycles and historical medical records to generate an ovarian function progression trajectory curve; extracting inflection point features from the curve and performing causal association rule mining to construct a logical chain between abnormal indicators and clinical outcomes; and determining conditions layer by layer according to the logical chain to output the final screening results. This application improves the accuracy of screening infertile patients with PCOS.

The invention discloses an AI-based automobile finance dynamic risk assessment system and method, and particularly relates to the technical field of artificial intelligence. Basic information of customers, including credit score, income condition, loan history, brand, model and age limit information of vehicles, is acquired, the customer information is processed by using a machine learning algorithm, and the credit risk of the customers is assessed; the method comprises the following steps: acquiring customer loan historical data, generating a corresponding credit score, judging the repayment capability and past default records of a customer based on the customer loan historical data, evaluating the repayment risk of the customer, estimating the value of a vehicle based on the brand, model and age limit information of the vehicle in combination with the market trend, judging the mortgage risk of the vehicle, and integrating the credit score, the repayment risk and the mortgage risk. And performing deep fusion analysis on the multi-dimensional risk factors by using an AI algorithm, calculating a final comprehensive risk level, and giving suggestions on whether to approve loan, loan amount and interest rate adjustment according to a preset risk threshold.

The invention belongs to the technical field of data processing, and particularly relates to a sulfur-containing sewage corrosion inhibitor effect evaluation method based on field data processing, and the method comprises the following steps: obtaining environmental physical quantity data of sulfur-containing sewage, and obtaining a corrosion deterioration index by combining reference parameters; acquiring an initial corrosion rate, and acquiring a net corrosion inhibition index in combination with the reference corrosion rate and the corrosion deterioration index; the dynamic environment fluctuation degree is obtained according to the historical corrosion deterioration index, and then the size of the self-adaptive sliding window is obtained; extracting a concentrated trend characteristic and a discrete distribution characteristic of the net corrosion inhibition index in the size interval of the self-adaptive sliding window to obtain a film forming stability coefficient; and comparing the film forming stability coefficient with a preset judgment threshold value, and outputting a corrosion inhibitor efficiency grade judgment result. According to the method, the real protection level of the agent under the complex working condition is objectively restored, so that the efficiency evaluation of the corrosion inhibitor is more accurate.

The invention discloses a track sleeper chipping disease detection system based on a PCA technology, and relates to the technical field of chipping disease detection, and the system comprises the steps: collecting an original sleeper image through an image collection module as a to-be-detected image; the sleeper chipping image analysis module extracts a data dictionary by collecting a normal sleeper image set, generates a data model through PCA algorithm dimensionality reduction, constructs a reference image in combination with projection transformation of a to-be-detected image, preprocesses the to-be-detected image, and makes a difference between the to-be-detected image and the reference image to obtain a comparison image. And judging a falling block disease region through threshold segmentation and connected region area calculation, and marking an output result. According to the method, high-efficiency and high-precision recognition of sleeper falling block disease detection is realized, and the method is effectively adaptive to scenes with complex sleeper surface textures and redundant high-dimensional data.

This invention discloses an intelligent generation algorithm and system for intersection traffic organization and traffic signs and markings, belonging to the field of intelligent transportation technology. The intelligent generation algorithm for intersection traffic organization and traffic signs and markings includes parameter input, logical algorithm, result output, iterative calculation, and scheme verification. It can assist users in realizing the intelligent generation of intersection traffic organization and traffic signs and marking schemes, getting rid of the dependence on the designer's personal experience in traditional traffic design schemes, greatly eliminating personal subjectivity, and ensuring the compliance of traffic design schemes. Furthermore, based on the intelligent generation algorithm for intersection traffic organization and traffic signs and markings, an intelligent generation system for intersection traffic organization and traffic signs and markings is proposed, which to a certain extent solves the problems of current traffic scheme design being cumbersome and complex, having high entry barriers, and being unable to guarantee scheme compliance. It has broad application prospects in optimizing the design of existing intersection traffic schemes and verifying and reviewing traffic design scheme drawings.

The invention discloses a myocardial cell systolic function analysis method, system and device and a medium. The method comprises the following steps: setting an import generation parameter based on a myocardial cell rhythmic systolic video sequence, splitting and reading frame by frame based on the myocardial cell rhythmic systolic video sequence, and converting to generate a grayscale image; calculating two adjacent frames of pixel point displacement vector fields based on the gray level image, acquiring flow field components in different directions based on the pixel point displacement vector fields, and converting to generate a time speed sequence curve; smoothing and filtering the processed signal based on a time-speed sequence curve, detecting and identifying a key time phase of a contraction period based on the time-speed sequence curve, and completing period judgment and inspection; and calculating contraction process kinetic parameters based on the period. According to the method, a one-key operation process of video import, pixel size setting, parameter calculation and result export is realized through a software interface, unmarked, high-throughput and automatic myocardial cell systolic function evaluation is realized, and technical support is provided for drug screening and disease model function research.

This invention discloses a method for obtaining the brittle-plastic transition depth of a two-phase composite material. The method involves using a metallographic microscope to capture a microscopic image of the scratch surface morphology. The locations of the first small pit and the first large pit in the scratch are determined from this image. Then, the distances from these two locations to the scratch incision point are measured using a measuring scale. The three-dimensional morphology of the scratch groove is obtained by photographing it using a white light interferometer. Based on the distances obtained from the measuring scale, the starting point (first small pit) and the ending point (first large pit) of the brittle-plastic transition zone are determined. The cross-sectional profile curve of the scratch at these locations is then captured, and the groove depth is measured. This invention combines the microscopic morphology captured by a microscope with the three-dimensional morphology obtained by white light interferometry to accurately determine the location and depth of the brittle-plastic transition in the material. This avoids interference from defects in the two-phase composite material itself or surface impurities and dust, effectively improving the accuracy of the test results.

The application discloses a kind of construction hoist standard section rack automatic positioning installation combination mechanism, more specifically related to machine tool combined machining technical field, including machine tool table, two side tables, two lifting strips and end checking assembly, the upper surface of machine tool table is fixed with two side tables;Two lifting strips are installed between two side tables;End checking assembly is installed on the side of lifting strip.The application has double checking of rack and standard section end, and multi-dimensional comprehensive checking of hole position, tooth groove and side surface position in the middle of rack and standard section, effectively avoids rework caused by end size inconsistency, reduces rework rate in later processing, reduces rework installation cost of machine tool combined machining, thereby solve the high error rate of installation combination, directly lead to rework rate in later processing greatly increases, greatly improve the combined machining cost of machine tool.

The invention discloses an acoustic radar near-surface wind speed inversion method and system based on entropy weight feature fusion, and the method specifically comprises the steps: carrying out the preprocessing of acoustic radar frequency spectrum data, and obtaining the preprocessed frequency spectrum data; based on the preprocessed spectrum data, constructing a Doppler window dynamic migration model based on turbulence dynamics power law distribution; on the basis of a Doppler window dynamic migration model, candidate spectrum peaks are selected at each height layer, a feature space is constructed, feature weights are dynamically distributed by using an information entropy theory, and an optimal spectrum peak is selected; introducing a symmetry constraint optimization strategy to the optimal spectrum peak, detecting the profile symmetry of the wind spectrum in the symmetric direction, and calibrating an asymmetric result to obtain a target wind speed inversion result; and comparing and evaluating the target wind speed inversion result and the wind speed of the anemometer tower as a reference to complete inversion result verification. According to the method, the wind speed inversion precision of the acoustic radar in a complex environment is effectively improved, and better evaluation and efficient utilization of wind energy resources are promoted.

The invention discloses an automatic coprocessing system and method for an automobile financial process, and belongs to the technical field of automobile finance. The system comprises a data acquisition standardization module for integrating multi-dimensional data and outputting a standard data set; the risk portrait module extracts parameters, generates labels and constructs a dynamic network; the risk prediction module quantifies the default probability and generates a personalized strategy; the intervention execution module automatically propels a process and records a log; the model strategy iteration module adjusts the edge weight, optimizes the strategy library, forms closed-loop iteration, and improves the risk management and control efficiency; according to the invention, through multi-dimensional data integration and dynamic risk network construction, comprehensive risk perception and tracking are realized, and the limitation of traditional single assessment is broken; in combination with risk conduction analysis and personalized intervention, by means of an automatic execution and closed loop iteration mechanism, manual deviation is reduced, market changes are dynamically adapted, decision accuracy and system performance are improved, and automobile financial risk management and control are enhanced.

The invention relates to the technical field of payment settlement, and discloses a scientific and technological achievement transaction fund automatic settlement method and system based on a smart contract, and the method comprises the steps: compiling a transaction parameter based on a transaction intention, obtaining a contract byte code, deploying the contract byte code to a block chain, and obtaining a unique contract address. The buyer locks transaction funds to the contract account, and meanwhile, an achievement delivery file uploaded by the seller is encrypted and fragmented to generate derived data segments; the derived data segments are distributed to distributed storage nodes, and storage addresses and content hash values returned by the nodes are combined into a fragment index list according to an agreed format; and extracting digital voucher information of the delivery file from the list, and performing byte verification on the digital voucher information and a seller digital voucher sample in the transaction parameters to obtain a content acceptance result. And finally, according to a settlement identifier in an acceptance result, automatically determining a transfer direction of funds of the two transaction parties. According to the invention, the efficiency of scientific and technological achievement transaction fund automatic settlement based on the smart contract can be improved.